Reading training examples...done
Training set properties: 25 features, 180 rankings, 15501 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.006856
Iter 1: .........*(NumConst=1, SV=1, CEps=1006.8556, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=786.5317, QPEps=0.0001)
Iter 3: .........*(NumConst=3, SV=2, CEps=1382.4071, QPEps=0.0002)
Iter 4: .........*(NumConst=4, SV=3, CEps=1616.4178, QPEps=0.0060)
Iter 5: .........*(NumConst=5, SV=4, CEps=1563.7099, QPEps=0.0024)
Iter 6: .........*(NumConst=6, SV=5, CEps=296.2552, QPEps=0.0005)
Iter 7: .........*(NumConst=7, SV=4, CEps=270.2888, QPEps=27.1448)
Iter 8: .........*(NumConst=8, SV=6, CEps=184.0124, QPEps=5.6709)
Iter 9: .........*(NumConst=9, SV=6, CEps=161.4064, QPEps=49.9766)
Iter 10: .........*(NumConst=10, SV=7, CEps=90.7359, QPEps=44.9782)
Iter 11: .........*(NumConst=11, SV=8, CEps=53.3695, QPEps=26.1779)
Iter 12: .........*(NumConst=12, SV=9, CEps=53.9106, QPEps=25.6275)
Iter 13: .........*(NumConst=13, SV=8, CEps=75.2629, QPEps=20.5339)
Iter 14: .........*(NumConst=14, SV=8, CEps=69.4885, QPEps=25.3569)
Iter 15: .........*(NumConst=15, SV=8, CEps=38.4679, QPEps=18.1409)
Iter 16: .........*(NumConst=16, SV=6, CEps=35.3179, QPEps=17.2972)
Iter 17: .........*(NumConst=17, SV=7, CEps=23.7873, QPEps=10.4847)
Iter 18: .........*(NumConst=18, SV=7, CEps=18.1649, QPEps=8.5623)
Iter 19: .........*(NumConst=19, SV=9, CEps=12.6776, QPEps=6.2513)
Iter 20: .........*(NumConst=20, SV=8, CEps=17.8306, QPEps=6.2591)
Iter 21: .........*(NumConst=21, SV=9, CEps=11.0008, QPEps=5.4544)
Iter 22: .........*(NumConst=22, SV=10, CEps=8.3582, QPEps=4.1320)
Iter 23: .........*(NumConst=23, SV=9, CEps=19.2875, QPEps=4.1583)
Iter 24: .........*(NumConst=24, SV=8, CEps=7.1468, QPEps=3.5063)
Iter 25: .........*(NumConst=25, SV=9, CEps=4.1835, QPEps=1.9308)
Iter 26: .........*(NumConst=26, SV=9, CEps=1.8602, QPEps=0.9113)
Iter 27: .........*(NumConst=27, SV=10, CEps=3.1521, QPEps=0.7925)
Iter 28: .........*(NumConst=28, SV=10, CEps=2.3587, QPEps=0.9223)
Iter 29: .........*(NumConst=29, SV=9, CEps=2.0083, QPEps=0.8084)
Iter 30: .........*(NumConst=30, SV=9, CEps=1.3833, QPEps=0.6742)
Iter 31: .........(NumConst=30, SV=9, CEps=0.7833, QPEps=0.6742)
Final epsilon on KKT-Conditions: 0.78334
Upper bound on duality gap: 0.00736
Dual objective value: dval=6.37371
Primal objective value: pval=6.38107
Total number of constraints in final working set: 30 (of 30)
Number of iterations: 31
Number of calls to 'find_most_violated_constraint': 5580
Number of SV: 9 
Norm of weight vector: |w|=0.44336
Value of slack variable (on working set): xi=628.10700
Value of slack variable (global): xi=628.27862
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=9399.43521
Runtime in cpu-seconds: 0.57
Compacting linear model...done
Writing learned model...done